However, not all aspects of human semantic knowledge are satisfactorily captured in terms of fuzzy relations and graded similarity.

our knowledge of the meaning of words denoting specific entities involves a number of "hard facts" about the referents they denote that are best formalized as attribute-value pairs, of the sort that are stored in manually-curated knowledge bases, such as FreeBase or Wikidata.


As an upper bound, we train a model that uses the same architecture as described above but uses as input not distributional vectors but the FreeBase attributes themselves.

this model has to learn "only" an identity mapping.


On numeric features, where the baseline predicts the mean, its performance is 0.35 NRS on both datasets.

its average prediction is off by about one third the length of the ranked list for each attribute.


Indeed, while systems have successfully been developed to model entailment between quantifiers, ranging from natural logic approaches (MacCartney and Manning, 2008) to distributional semantics solutions (Baroni et al., 2012), they rely on an explicit representation of quantification.

they can model the entailment All koupreys are mammals |= This kouprey is a mammal, but not Koupreys are mammals |= This kouprey is a mammal.


We complement such research by providing a more formal interpretation of the mapping between language and world knowledge.

we offer a) a vectorial representation of set-theoretic models; b) a mechanism for predicting the application of generalised quantifiers to the sets in a model.


Our claim is that this very underspecified model can be further specified by learning a function F from distributions to generalised quantifiers.

where w k is the distribution of w k and Q 1 ...Q n ∈ {no, f ew, some, most, all} .


Specifically, where w k is the distribution of w k and Q 1 ...Q n ∈ {no, f ew, some, most, all} .

f takes a distribution w k and returns a quantifier for each predicate in the model, corresponding to the set overlap between w k and p 1...n .


The focus of this paper was concept-predicate pairs out of context.

we considered quantified sentences where the restrictor was the entire set denoted by a lexical item.


A higher RP indicates that the English translation of this Japanese phrase will likely be followed by the translation of the next Japanese phrase.

translation of the two consecutive Japanese phrases is monotonic, thus, we can begin translating immediately.


The volume of text available on social media provides a new opportunity for public policy and political science.

in the area of international relations, advances in natural language understanding and sentiment analysis may offer new insights into the sentiment of one nation toward another.


Given limited and sometimes conflicting results in previous research, in this study, we want to systematically investigate the relationship between a comprehensive set of personal traits and brand preferences.

we want to shed some light on (1) whether it is possible to use personal traits to predict consumer's brand preferences?


Our trait survey includes five parts designed to measure three types of personal traits: personality, values and needs plus demographics and personal interests.

since the Big-Five model of personality is the most popular model of personality traits among personality psychologists, we adopted a standard survey for Big 5 personality.


given input text authored by a user (e.g., tweets), our system computed the word counts of different psychologically-meaningful word categories defined in the Linguistic Inquiry and Word Count (LIWC) dictionary (Pennebaker et al., 2001).


Based on a preliminary evaluation with 250 participants, more than 80 percent of them, scores for traits that were inferred for all three models correlated significantly with survey-based scores (p<0.05 and correlation coefficient between 0.05 and 0.8).

scores that were derived by our system correlated with survey-based scores for 80.8% of participants' Big Five scores (p<0.05 and correlation coefficients between 0.05 and 0.75), for 86.6% of participants' Needs scores (p<0.05 and correlation coefficient between 0.05 and 0.8), and for 98.21% of participants' Values scores (p<0.05 and correlation coefficients between 0.05 and 0.55).


Moreover, the participants also rated on a five-point scale how well each derived characteristic matched their perceptions of themselves, and their ratings suggest that the inferred characteristics largely matched their self-perceptions.

means of all ratings were above 3 ("somewhat") out of 5 ("perfect"): 3.4 (with a std.


We use the overall rank data in Table 10 as our baseline.

for each product category, the baseline always ranks all its brands based on the average ranks defined in Table 10.


We assume that, similar to Wikipedia, the entity prominence also influences how users are exposed and spread the hashtag on Twitter.

the initial spreading of a trending hashtag involves more entities in the focus of the topic.


Based on this assumption, we propose to gauge the entity prominence as its potential in maximizing the information spreading within all entities present in the tweets of the hashtag.

the problem of ranking the most prominent entities becomes identifying the set of entities that lead to the largest number of entities in the candidate set.


Noticing that KL divergence is not symmetric, both KL(P Q) and KL(Q P ) are computed.

smoothing is performed while computing KL(Q P ), where we use the same setting as in Louis and Nenkova (2013).


For example, we want to make sure we translate chair to stuhl in German, and not to tisch.

what we are inter-ested in for this particular task is genuine similarity, rather than relatedness.


Since cross-entropy layer is only associated with L S , we need to traverse the transformation parameters to obtain sentiment distribution for each node (green path in Fig.2).

we first transform p t to source side phrase p ′ s and then apply the cross entropy weights to it.


The main contribution of this paper is therefore to learn to predict the most suitable preposition given its context, and to learn this jointly from images and their descriptions.

we concentrate on learning from (i) geometric relations between two visual entities from image annotations; (ii) textual features from textual descriptions; (iii) visual features from images.


Therefore, optimization methods for IBM Model 1 (specifically, the EM algorithm) are typically only guaranteed to reach a global maximum of the objective function (see the Appendix for a simple example contrasting convex and strictly convex functions).

although the objective cost is the same for any optimal solution, the translation quality of the solutions is not fixed and will still depend on the initialization of the model (Toutanova and Galley, 2011).


For our concrete experiments, we picked a model based on Lemma 1 and used h( Using this setup, parameter estimation for the new model can be accomplished via a slight modification of the EM algorithm for IBM Model 1.

we have that the posterior probabilities of this model factor just as those of the standard Model 1 and we have an M step that requires optimizing � a (k) q(a (k) |e (k) , f (k) ) log p(f (k) , a (k) |e (k) ) 1: Input: Define E, F , L, M , (e (k) , f (k) , l k , m k ) for k = 1 .


The performance of our new model will rely heavily on the choice of α(e j ) ∈ (0, 1) we use.

we could make β depend on the association between the words, or the words' positions, or both.


However, we stress that the main concern of this work is not just heuristicbased empirical improvement, but also structured learning.

although using an regularizer l 2 and the methods of (Moore, 2004) would yield a strictly concave version of IBM 1 as well (with improvements), it is not at all obvious how to choose the learning rate or set the penalty on the lexical parameters.


Where l −m is the number of components representing the last 20 % of the original mass.

the function f : Σ →Σ performing this redistribution is such that: In this formulation, we can consider the Caron ptransform and the PC-removal scheme as special cases, where the Caron p-transform is given by: and the PC-removal scheme by: where δ(F ) denotes the generalized Kronecker delta function.


The new topic model we propose therefore takes association links into account so that a document's topic distribution is influenced by the topic distributions of its neighbors.

we propose a joint model that uses link structure to define clusters (cliques) of documents and, following the intuition that documents in the same cluster are likely to have similar topic distributions, assigns each cluster its own separate Dirichlet prior over the cluster's topic distribution.


In this context, embedding models (Wang et al., 2014;Lin et al., 2015;Jenatton et al., 2012;Socher et al., 2013) that attempt to learn lowdimensional vector or matrix representations of entities and relationships have shown promising performance in recent years.

the basic model TRANSE (Bordes et al., 2013) has been proved to be very powerful.


Then, we filter out paths where (h, 1 , e) = (h, , t) or (e, 2 , t) = (h, , t), as well as the paths with 1 = 2 and h = e = t. We focused on "unambiguous" paths, so that the reasoning might actually make sense.

we considered only paths where 1 is either a 1-to-1 or a 1-to-many relationship, and where 2 is either a 1-to-1 or a many-to-1 relationship.


In this work, we explore several approaches to measuring the quality of neural word embeddings.

we perform a comprehensive analysis of evaluation methods and introduce novel methods that can be implemented through crowdsourcing, providing better insights into the relative strengths of different embeddings.


First, this is the first paper to conduct a comprehensive study covering a wide range of evaluation criteria and popular embedding techniques.

we study how outcomes from three different evaluation criteria are connected: word relatedness, coherence, downstream performance.


We can estimate the accuracy of dependency subsets such as P 100 , P 80 , P ≥7 and so on by comparing these dependencies to the dependencies from a supervised German parser on the same data.

we use a supervised parser to provide gold standard annotations.


The model is based on a transition-based dependency parser (Nivre, 2006) formulated as a neural-network classifier to decide which transition to apply to each parsing state configuration.

2 for each configuration, the selected list of words, POS tags and labels from the Stack, Queue and Arcs are extracted.


Generally speaking, we can understand the model as building the universal dependency parser that parses the universal language.

the model is the combination of two parts: the universal part (W 1 , W 2 ) that is shared between the languages, and the conversion part (E s , E t ) that maps a language-specific representation into the universal language.


As suggested by Chen and Manning (2014), we apply drop-out, a form of regularization for both source and target language.

we randomly drop some of the activation units from both hidden layer and input layer.


's parser can be understood as the sequential manipulation of three data structures: a buffer B initialized with the sequence of words to be parsed, a stack S containing partially-built parses, and a list A of actions previously taken by the parser.

the parser implements the arc-standard parsing algorithm (Nivre, 2004).


Botha and Blunsom (2014) show that stems, prefixes and suffixes can be used to learn useful word representations but relying on an external morphological analyzer.

they learn the morpheme-meaning relationship with an additive model, whereas we do not need a morphological analyzer.


While phenomena like long-distance relations may seem to make generation of grammatically correct compressions impossible, we are going to present an evidence to the contrary.

we will present a model which benefits from the very recent advances in deep learning and uses word embeddings and Long Short Term Memory models (LSTMs) to output surprisingly readable and informative compressions.


The deletion approach is a standard one in compression research, although the problem is often formulated over the syntactic structure and not the raw token sequence.

one usually drops constituents or prunes dependency edges (Jing, 2000;Knight & Marcu, 2000;McDonald, 2006;Clarke & Lapata, 2008;Berg-Kirkpatrick et al., 2011;Filippova & Altun, 2013).


Both the LSTM systems we introduced and the baseline require a training set of a considerable size.

the LSTM model uses 256dimensional embeddings of token sequences and cannot be expected to perform well if trained on a thousand parallel sentences, which is the size of the commonly used data sets (Knight & Marcu, 2000;Clarke & Lapata, 2006).


Arguably, such cases would also be difficult for other systems.

recognizing when the context is crucial is a problem that can be solved only by including deep semantic and discourse features which has not been attempted yet.


Even though for a significant number of input sentences the compression was a continuous subsequence of tokens, there are many discontinuous compressions.

the LSTM model learned to drop appositions, no matter how long they are, temporal expressions, optional modifiers, introductory clauses, etc.


We build an N-gram word ordering system under the same beam-search framework as the syntactic word ordering system.

search is performed incrementally, from left to right, adding one word at each step.


We say a system is abstractive if it tries to find the optimal sequence from this set Y, under a scoring function s : X × Y → R. Contrast this to a fully extractive sentence summary 3 which transfers words from the input: or to the related problem of sentence compression that concentrates on deleting words from the input: While abstractive summarization poses a more difficult generation challenge, the lack of hard constraints gives the system more freedom in generation and allows it to fit with a wider range of training data.

in this work we focus on factored scoring functions, s, that take into account a fixed window of previous words: where we define y c y [i−C+1,...,i] for a window of size C. consider the conditional logprobability of a summary given the input, s(x, y) = log p(y|x; θ).


These can be computed as a mini-batch, which in practice greatly reduces the factor of K. While we will see that the attention-based model is effective at generating summaries, it does miss an important aspect seen in the human-generated references.

the abstractive model does not have the capacity to find extractive word matches when necessary, for example transferring unseen proper noun phrases from the input.


We propose an approach to overcome the aforementioned shortcomings of existing scientific summaries.

we extract citation-context in the reference article for each citation.


Evaluation results on the TAC2014 dataset show that our proposed methods can effectively improve over the well-known existing summarization approaches.

we obtained greater than 30% improvement over the highest performing baseline in terms of mean ROUGE scores.


While citations are useful for summarization, relying solely on them might not accurately capture the original context of the referenced paper.

the generated summary lacks the appropriate evidence to reflect the content of the original paper, such as circumstances, data and assumptions under which certain findings were obtained.


To extract communities from the graph of reference spans, we use the algorithm proposed by (Blondel et al., 2008) which is a simple yet accurate and efficient community detection algorithm.

communities are built in a hierarchical fashion.


• Iterative: We select top sentences iteratively from each group until we reach the summary length threshold.

we first pick the top sentence from all groups and if the threshold is not met, we select the second sentence and so forth.


We can clearly observe that our proposed methods achieve encouraging results in comparison with existing baselines.

for 100 words short summaries, the discourse based method (with 34.6% mean ROUGE-L improvement over the best baseline) and for 250 word summaries, the community based method (with 3.5% mean ROUGE-L improvement over the best baseline) are the best performing methods.


We effectively achieved improvement over several well known summarization approaches on the TAC2014 biomedical summarization dataset.

in all cases we improved over the baselines; in some cases we obtained greater than 30% improvement for mean ROUGE scores over the best performing baseline.


We denote the coupled TF-IDF matrix as M * , obtained by the following formula.

three homogenous coupled TF-IDF matrices M sur , M lex and M syn can be built according to the three word coupling matrices C * .


Most of the recent AES related work is promptspecific.

an AES system is trained using essays from a specific prompt and tested against essays from the same prompt.


In this setting, it can be shown that the correlation between latent scoring functions for the target and the source tasks is ρ.

this, in fact, is a generalization of the EasyAdapt scheme, for which the correlation ρ is fixed at 0.5 [ (Daumé III, 2007), see eq.


we formally define the task of evidence detection, introduce an architecture for attacking this problem, and demonstrate its performance over dedicated manually labeled data.


Further, on average these 2.17 CDEs together span 1.5 sentences, whereas an average article in our data consists of 150 sentences.

even for claims with at least one CDE of type Study, on average only 2% of the sentences in the claim's article are part of such Study CDE.


• Lexicons -including external lexicons (the Harvard IV-4 dictionary) and manually and automatically compiled in-house lexicons.

for each evidence type, we manually compiled a lexicon of words characterizing this type by looking at examples from the held-out data.


These properties are assessed by measuring the semantic relatedness between (i) the claim and the content of the claim's article and (ii) the claim and topic.

• Properties of claim's article : we mainly consider the scores provided by the context-dependent component to all candidates examined in the claim's article.


Impact of context free stage: Comparing the pipeline performance to the baseline using only the context dependent component (NCFS baseline), the results indicate the necessity of the context-free stage in our pipeline.

assessing the coherence of candidates, as well as their evidence characteristics, seems to be essential to properly address CDED.


That is, assessing the coherence of candidates, as well as their evidence characteristics, seems to be essential to properly address CDED.

the fact that the gain is observed both in the MRR measure and in the MRR-overlap measure suggests that both the context-free components are valuable.


For quality, we compare the search result sets for the autocorrected queries with those of the reference and get an estimate of how similar each autocorrected query behaves to the expected behavior given by its gold correction.

we extract the categories from all items which are returned as part of the search API calls (see Table 7).


To directly handle the problem of discriminative learning when labeled data is scarce, (Ororbia II et al., 2015) proposed deep hybrid architectures that could effectively leverage small amounts of labeled and large amounts of unlabeled data.

the best-performing architecture was the Stacked Boltzmann Experts Network (SBEN), which is a variant of the DBN.


Word embeddings for language processing Recently many NLP systems have obtained improved performance with less human engineering by adopting distributed word representations (Socher et al., 2012a).

neural word embedding techniques are now known to be effective in capturing syntactic and semantic relationships, and more computationally efficient than many other competitors (Mikolov et al., 2013a;Pennington et al., 2014;Baroni et al., 2014).


Our baseline click prediction model is formulated as a supervised learning problem.

we use Logistic Regression (LR) since LR is well suited for probability estimation.


This is different from simply applying the same text extraction pipeline, that created the original KB, to dynamic Web content.

our approach has the following advantages: (1) Consider for example the SPOUSE relation, both marry and divorce are good patterns for extracting this relation.


Our approach learns state changing verbs from Wikipedia revision history.

we seek to establish a correspondence between infobox edits and verbs edits in the same article.


Another baseline is developed with the output of MRW LP (LP-baseline) that contains labeled lists and mentions.

the labeled coordinate lists are broken into items each of which has the list class, and evaluation is conducted with these items together with the labeled mentions as positive predictions.


The evaluation data is generated with the validating set of each run.

for DIEL and LP-baseline, the evaluation data is prepared with the top 500 lists (singleton and coordinate lists) of each type, as scored by MRW with the validating instances as seeds.


When such a KG is used in an application, one is often interested in known facts for a given entity, and not necessarily the overall size of the KG.

knowing the average number of facts per entity is quite informative.


In addition, we add "creative" rules that are not necessary for English expressions but might match expressions in other languages.

we allow several orderings of patterns and sometimes even random tokens in between.


If, at a later point, the automatically created resources are used as basis for developing better resources for specific languages, missing translations can be easily detected and manually added.

for the rule development process, we also create the automatic resources for English.


Clearly, more work is necessary to achieve temporal tagging quality that can be considered as applicable for other applications.

issues with morphology-rich languages and those without white-space tokenization have to be addressed.


We explore several types of embeddings for Chinese text and their effect on Chinese social media NER.


First, we observe that the results for the baseline are significantly below those for SIGHAN shared tasks as well as the reported results on Twitter NER, showing the difficulty of this task.


SP is based on the idea of mutual information (Erk, 2007), which tends to select types which are unique to the relation.

common types which can be used for many different relations are less preferred.


The DNN-based feature transformation generally yielded better results.

it becomes very effective in conjunction with recursive feature averaging.


Here we try to help the user by deciding automatically which answers are good and which are bad for a given question.

we focus on exploiting the output structure at the thread level in order to make more consistent global decisions.


In contrast, we postulate that in a CQA setting, the answers from different users in a common thread are strongly interconnected and, thus, a joint answer selection model should be adopted to achieve higher accuracy.

we focus on the relations between two comments at any distance in the thread.


We define a set of binary variables, whose assignment will univocally define the classification of all comments in the thread.

we define a pair of variables for each answer: x iG and x iB , 1 ≤ i ≤ n. Assigning 1 to x iG means that comment c i in the thread is classified as Good; assigning it 0 means that c i is not classified as Good.


In this paper, we present a novel application of sentiment analysis for exploring attitudes and opinions about risk in textual disclosures by banks.

this work (1) finds suitable data sources, (2) identifies appropriate techniques for risk sentiment analysis, and (3) analyzes risk sentiment within the last decade in order to cover the financial crisis of 2007-08 adequately.


The T1 is one of the most important ratios based on risk-weighted amount of the bank's assets.

it refers to the bank's Tier 1 capital as a percentage of its risk-weighted assets: (1) Tier 1 capital is considered as the best form of bank capital and has to fulfill several criteria making it relatively secure.


In the first step of the analysis, sentiment words in the textual data are tagged.

this study works with negative words (Fin-Neg), positive words (Fin-Pos), and words related to uncertainty (Fin-Unc).


After calculating the sentiment scores, the data are filtered and grouped in order to prepare them for the evaluations.

the data are filtered according to specific countries and grouped by year respectively by bank.


While the reviewed aspects differ between domains, our assumption is that the overall argumentation of a web review is generally represented by a sequence of local sentiments, called the review's sentiment flow (Mao and Lebanon, 2007).

we hypothesize that, under an adequate model, similar sentiment flows express similar global sentiments, also across domains.


As we assume that the overall argumentation of a web review represents global sentiment in the first place, we fully abstract from the content of the facts and opinions that serve as arguments.

we model the argumentation of a web review solely by its sentiment flow, i.e., the sequence of local sentiments comprised in the review's text.


Here, we investigate more informed ways of abstracting flows.

we consider three transformations of flows: Change Deletion of repeating local sentiments.


Global Sentiment To determine global sentiment, we perform supervised learning based on the feature types outlined above.

we use the default configuration of the random forest classifier from Weka (Breiman, 2001;Hall et al., 2009) without any parameter optimization.


This paper puts the goal of domain independence in the sentiment analysis of web reviews into the focus.

we hypothesize that an abstract model of the local sentiment flow in a review generally captures the review's overall argumentation regarding global sentiment.


If the restriction by a CFO is associated with a user-related perspective, we call such CFOs "userrestrictive CFOs (U-CFOs)".

target users to whom an opinion unit is relevant are restricted by its corresponding U-CFO, although those users may agree or disagree with the opinion.


If we fully utilize U-CFOs, as discussed for the active solution above, we need to classify U-CFOs into semantic categories so that users can selectively read relevant opinions.

the identification for U-CFOs facilitates predicting the review helpfulness (O'Mahony and Smyth, 2010;Moghaddam et al., 2012).


The main idea is to propagate trust within a graph based on our observations that true mentions are more similar in context than false mentions in a specific domain.

inverted indexes is used to construct the graph locally that allows for an arbitrary number of target entities and documents.


We link only similar nodes by choosing an appropriate threshold η.


Towards the bottleneck problem between comprehensiveness and definiteness of acquisition, we propose a collaborative archiving method.

we introduce topic modeling methodologies into entity biography profiling, so as to build a bridge between fuzzy and exact matching.


Fuzzy matching (FM) relaxes the conditions to a large extent, allowing Q to be split into nonadjacent words.

it supports the change in word order as well as partial match.


(2013) tests for a wide variety of knowledge and its application to specific scenarios.

we focus on a subset that test students' understanding of various kinds of general rules and principles (e.g., gravity pulls objects towards the Earth) and their ability to apply these rules to reason about specific situations or scenarios (e.g., which force is responsible for a ball to drop?).


Graph Alignment Rules: Similar to the previous approaches, we use entailment scores between words and short phrases to compute the alignment.

in addition, we also expect aligned nodes to have similar edge structures: if node x aligns with y then their children/ancestors should also align.


As Table  2 shows, Praline's accuracy drops upon removing   either of these constraints, highlighting their importance.

when using only one KB rule, dropping FUP clauses has a bigger influence that dropping Acyclic constraint clauses.


Granted, we can only assert co-occurrence between two connected mentions in G m , but it should be of great probability that two co-occurring mentions have their entity referents connected by some relation in the KB.

the more a G i c is structurally similar to its G m , the better the candidates in this G i c represent their mentions in G m .


those mentions within the same context window) mostly have similar relations in the KB.

for each mention, the candidate entity types are not as diverse as those in the general domain.


The objective will favor lower scores for valid triples as compared with invalid triples.

the invalid triple set with respect to (h, r, t) is defined as the set of invalid triples is composed of the original valid triple (h, r, t) with one of three components replaced.


We randomly select a valid triple from the training set iteratively for learning.

in the implementation, we also enforce constraints on the norms of the embeddings h, r, t. we set There are also some implementation details that will significantly influence the performance of representation learning, which are introduced as follows.


Hence, we add reverse relations for each relation in KBs.

for each triple (h, r, t) we build another (t, r −1 , h).


These methods as well as standard NER systems try to maximize correct classification of mentions in individual contexts whereas we aggregate individual contexts and evaluate on accuracy of entity-type assignments inferred from the entire corpus.

their evaluation is sentence-level whereas ours is corpus-level.


We aim to learn a scoring function S c2t that can distinguish these cases.

based on the context scoring function S c2t , we then compute the corpus-level CM scoring function S CM that takes the scores S c2t (c i , t) for all contexts of entity e in the corpus as input and returns a score S CM (e, t) that assesses the appropriateness of t for e. S CM is: where U e,t = {S c2t (c 1 , t), .


We do not have such a dataset in our problem setting, but we can use the contexts of linked entities as distantly supervised data.


A well-trained CM will also work for cases for which GM is not applicable.

if the KB contains only a small number of entities of a particular type, but the corpus contains a large number of contexts of these entities, then CM is more likely to generalize well.


Our work leverages the factored approach, but improve performance by using a continuous-space sentence representation for predicting presence of a name.

we modify a recurrent neural network (RNN) language model (LM) to predict both the word sequence and a sentence-level name indicator.


In our work, the problem is factored to use the acoustic confusibility and local word class features for OOV error prediction, which is combined with the sentence-level name posterior for name error prediction.

only two features (posteriors) are used in training with the sparse name error prediction data.


In fact, this is roughly the approach adopted by our participating system in SpaceEval (D'Souza and Ng, 2015), which achieved the best results in one of the SpaceEval tasks involving MOVELINK extraction.

this system trains one classifier for each optional role r to identify the filler for r in a MOVELINK independently of the other optional roles.


This novel combination of sieves and tree-based structured features results in what we call an expanding parse tree.

as a spatial element for a MOVELINK is extracted by a (role-specific) sieve, it will be added to the structured feature for the classifier associated with the following sieve.


Specifically, as a spatial element for a MOVELINK is extracted by a (role-specific) sieve, it will be added to the structured feature for the classifier associated with the following sieve.

the parse tree corresponding to the structured feature will keep expanding as we move along the sieves in the pipeline.


Given a set of spatial elements with an assignment of roles to each element, a joint spatial relation extraction system uses a binary classifier to determine whether these elements form a spatial relation with the roles correctly assigned to all participating elements.

the classifier will output a 1 if and only if (1) the elements in the set form a relation and (2) their roles in the relation are correct.


1 This Baseline system performs joint role labeling and relation classification using an ensemble of classifiers.

it trains one classifier for extracting QSLINKs and OLINKs, and seven classifiers for extracting MOVELINKs.


All other learning parameters are set to their default values.


As in the LINK classifier, we enforce global role constraints when creating training instances for the MOVELINK classifiers.

the roles assigned to the spatial elements in each training instance of each MOVELINK classifier are subject to six constraints: (1) the trigger has type motion event; (2) the mover has type place, path, spatial entity, or non-motion event; (3) the source, the goal, and the landmark can be NULL or have type place, path, spatial entity, or non-motion event; (4) the midpoint can be NULL or have type place, path, or spatial entity; (5) the path can be NULL or have type path; and (6) the motion signal can be NULL or have type motion signal.


We set sieve 0 to be the LINK classifier and sieve 1 to be the (trigger, mover) classifier, and then order the remaining sieves by precision.

we compute the precision of each sieve on the development data, then add sieves into the pipeline in decreasing order of precision.


However, our first method of using sieves makes limited use of the decisions made by earlier sieves.

while each sieve exploits the knowledge of whether a spatial element has been assigned a role by an earlier sieve, it does not exploit the knowledge of what the role is.


To strike a better balance between having a rich representation of the context surrounding a spatial relation and improving the learner's ability to generalize, we extract a subtree from a parse tree and use it as the value of the structured feature.

given relation candidate triplet (e 1 ,e 2 ,e 3 ), where spatial elements e 1 , e 2 , and e 3 are posited in roles r 1 , r 2 , and r 3 , respectively, and the associated syntactic parse tree T , we extract our parse subtree from T as follows.


Recall that a structured feature is used as an additional feature when training each classifier.

it will be used in combination with the original 31 features.


To train a classifier on instances containing the original 31 features and SRCTs, we employ SVM light−T K (Moschitti, 2004;Moschitti, 2006), which (1) trains an SVM classifier using the 31 features with a linear kernel; (2) trains an SVM classifier using only the SRCTs with a convolution  kernel; and (3) combines these two kernels using a composite kernel.

we define composite kernel K c for combining linear kernel K l and convolution kernel K t as follows: where F 1 and F 2 are the set of 31 features from two training instances, T 1 and T 2 are their SRCTs, and T is the combination parameter.


Figure 4(a) shows the SRCTs generated when each sieve makes the correct decisions.

sieve 1 correctly identifies "He" as the mover and "biked" as the trigger.


Note that VTK considers random walks in dependency trees instead of downward paths, i. e. potentially taking into account same nodes multiple times for phrase length greater than two, phenomenon known as tottering.

once we have phrases to compare, we may construct a kernel between them as the product of word kernels if they are of the same length l. we define the Product Kernel (PK) as: where w j i is the i-th word in phrase p j of length l. Alternatively, in particular for phrases of different lengths, we may embed phrases into the embedding space by taking a composition operation on the constituent word embeddings.


We focus on incorporating topic-level knowledge, which is more challenging, as seed/prior topics could be latent rather than observable.

we construct our novel TSDPM-M (Topic Seeded DPMM) based on a principled seeded Pólya urn (sPU) scheme.


Experimental results on document clustering across three corpora demonstrate that our model effectively incorporates prior topics, and significantly outperforms state-of-the-art DPMM model.

our TSDPMM can be applied in a lifelong learning framework which enables the prior topic knowledge to evolve as more and more data are observed.


(2014), we further propose a gated recursive neural network (GRNN) by introducing two kinds of gates, namely "reset gate" and "update gate".

there are two reset gates, r L and r R , partially reading the information from Gate rL Gate rR h2j (l-1) h2j+1 (l-1) hĵ (l) hj (l) Figure 3: Our proposed gated recursive unit.


Improving the search and browsing experience in PubMed is a key component in helping users detect information of interest.

when exploring a novel field, it is important to provide a comprehensive view for a specific subject.


Apart from single classification mistakes, mainly due to unusual section titles that do not appear in the training set (such as "Anathematization" in Pope Honorius I page) 9 , we found that some wrong classifications depended on specific types of persons in our data set.

the classifier tends to assign a positive label to sections in the pages of mythological characters, even if they cannot have a biography because they did not exist.


We also performed additional experiments in order to investigate the impact of the size of the training data on the classification task.

we extended our training data by creating new training sets based on 25,000, 50,000, 75,000 and 100,000 Wikipedia pages.


This leads to an efficient learning algorithm called constraint generation (Koller and Friedman, 2009;Felzenszwalb et al., 2010).

an initial model is trained by a randomly selected subset of the constraints.


But such information can be recovered by the features that include the information of the question sentence.

we associate a number with all the nouns in the same sentence sorted by the length of the dependence path between them.


Roman Urdu lacks standard lexicon and usually many spelling variations exist for a given word, e.g., the word zindagi [life] is also written as zindagee, zindagy, zaindagee and zndagi.

the following normalization issues arise: (1) differently spelled words (see example above), (2) identically spelled words that are lexically different (e.g., bahar can be used for both [outside] and [spring], and (3) spellings that match words in English (e.g, had [limit] for the English word 'had').


It incorporates an assignment similarity threshold, t > 0, for controlling the number of clusters and their similarity.

it ensures that all words in a cluster have a similarity greater than or equal to this threshold.


It is important to note that the poular k-means algorithm is known to be effective for numeric datasets only which is not true in our case, and it cannot utilize our specialized similarity function for lexical variation discovery.

lex-C starts from an initial clustering based on UrduPhone or string similarity.


A lot of times, they allow us to assumingly understand or infer the meanings of characters without any context.

the component-level features inherently bring with additional information that benefits semantic representations of characters.


Consequently, when minimizing the global loss function, the weights of the k-th classifier are updated according to not only the loss of the k-th classifier but also the losses of the latter classifiers.

feedbacks are propagated from the latter classifiers to the k-th classifier.


These nodes are used to sequentially arrange mentions with different left boundaries.

each A node at position k (the k-th word), or A k , is used to compactly represent all such mentions in the sentence whose left boundaries are exactly at or strictly after k. • E nodes.


Following the conditional random fields (Lafferty et al., 2001), we adopted a log-linear approach for such a joint mention extraction and typing task.

for a given input sentence x, the probability of predicting a possible output y (a mention sub-hypergraph that represents a particular combination of mentions) is given as follows: where f (x, y) is the feature vector defined over the input-output pair (x, y), and the weight vector w gives the parameters of the model.


1 The features that we use are inspired by the work of (Carreras et al., 2002).

we consider the following features defined over the inputs: • Words (and POS tags, if available) that appear around the current word (with position information), with a window of size 3.


To understand how the features and speed change as we increase the number of mention types (i.e., semantic types), we also conducted experiments where we increase the number of possible mention types.

we created subtypes from each original type annotated in the dataset.


We empirically captured the relationship between the speed of each system (average number of words processed per second) and the number of mention types.

we found that as we linearly increased the number of mention types, for the linear-chain CRF model, the number of features grew quadratically and the speed dropped quadratically, whereas for our model, the number of features grew linearly and the speed dropped linearly.


We compared our system's results with those of several baseline approaches based on CRF where the cascaded BILOU approach described above was always used.

we considered approaches that always regarded the complete span (CC-S), the first word (CC-F), and the last word (CC-L) as the predicted mention's head, respectively.


We followed the description of Finkel and Manning (2009) to set up our experiments on the GE-NIA dataset.

we used the first 90% of the sentences as the training data and the remaining 10% as the evaluation data.


We note that, however, these results are not directly comparable.

both of these two previous models relied on an additional 200 million words from PubMed abstracts to learn word clusters as additional features, which we do not have access to.


If NER is highly confident on its outputs of entity boundaries and types, it will encourage entity linking to link an entity which is consistent with NER's outputs, and vice versa.

jERL is able to model how consistent NER and linking's outputs are, and predict coherent outputs.


The usual solution is to prune or otherwise approximate the messages at each step.

cotterell and Eisner (2015) gave a principled way to approximate the messages using variablelength n-gram models, using an adaptive variant of Expectation Propagation (Minka, 2001).


For purposes of MAP inference, we define F to return the maximum of these path scores.

we take the WFSMs to be defined with weights in the (max, +) semiring (Mohri et al., 2002).


To handle the above challenges without approximation, we want to decompose the original problem into subproblems where each subproblem can be solved efficiently.

we want the subproblems to be free of cycles and highdegree nodes.


For example, our experiments partition Figure 1 as shown in Figure 2.

we partition the factors into K sets F 1 , .


We follow Paul and Eisner (2012) and use a substring count feature for each w ∈ Σ * .

γ(x) is an infinitely long vector, which maps each w to the number of times that w appears in x as a substring.


we choose φ ∈ [0.65, 1) and θ ∈ [0.25, 1) such that the predicted forms maximize the joint score (1) (always using the (max, +) semiring).


12 We follow Paul and Eisner (2012, section 5.1) fairly closely.

: Our stepsize in (5) is η = α/(t + 500), where t is the iteration number; α = 1 for Model 2S and α = 10 otherwise.


To break these ties, we add "jitter" separately to each copy of M φ in Figure 1.

if F i is the unary factor attached to X i , we expand our 0-gram model  6 Experimental Results As linguists know, reconstructing an underlying stem or suffix can be difficult.


He then gets back negative responses to his question from inca, Cheshire-CatGrin and dmm; and then he broadcasts in 239 an acknowledgment of all the negative responses.

we have 235, 236 & 238 all attached to 234 as answers to the question in 234; and we have 239 that is attached to 235, 236 & 238 as an acknowledgment of the contents of those turns.


This will require then rethinking the task of discourse parsing when attempting to learn such structures.

the following questions present themselves: 1) how many non-treelike structures are there?


In MaxEnt, the values for the different parametersŵ are obtained by maximizing the log-likelihood of the training data T with respect to the model (Berger et al., 1996): Given our observations about the structure of dialogues in our corpus, we hypothesize that a dialogue is fundamentally sequential: first one person talks and then others react to them or ignore them, but the discourse links that do occur between speaker turns are reactive.

a turn can't be anaphorically and rhetorically dependent on a turn that comes after it.


The inter-turn model on the other hand does not contain any backward links during learning.

it takes as input all pairs of EDUs (i, j) with i < j.


The reported results implement the turn-constraint during training for the local models.

training instances for the local models include only forward links.


Assuming that the semantic frame f of the predicate has already been identified (see §4.2 for this step), we seek to predict the set of spans that correspond to its overt semantic arguments and to label each argument with its semantic role.

we model the problem as that of assigning each span s ∈ S, from an over-generated set of candidate argument spans S, to a semantic role r ∈ R. The set of semantic roles R includes the special null role ∅, which is used to represent non-overt arguments.


The frame f and role r are initially encoded using a one-hot encoding as i f and i r .

i f and i r have all zeros except for one position at f and r respectively.


Both the tagger and the parser, as well as the SRL models use word cluster features (see Table 1).

we use the clusters with 1000 classes from Turian et al.


We assume the probability of each span is independent: We break this distribution into three cases.

to help describe the separate cases we define the function origin(s, C) to determine the origin index of the connection in C to the span s. origin(s k ij , C) = o ⇔ ∃(o, i, j, k, t syn , t sem ) ∈ C. Part-composite model When the encompassing argument is a food and the origin is a previous verb (i.e., P (s k ij |t syn ij , t sem ij = f ood, origin(s k ij ) = 0, C, h i )), then the probability of the span depends on the ingredients that the span represents given the connections in C. For example, "dressing" is more likely given ingredients "oil" and "vinegar" than given "chicken" and "noodles".


So far the computational semantics of comparatives and how they affect the meaning of text has not been studied effectively.

the difference between the existing semantic and syntactic representation of comparatives is not distinctive enough for enabling deeper understanding of a sentence.


One common criticism of research based on use of hashtags as gold labels is that the training utterances could be noisy.

tweets might be sarcastic but not have #sarcasm or #sarcastic hashtags.


Lexical-syntactic pattern (LSP): This measure is to find how much more evidence for t 1 t 2 is found in the Web than for t 2 t 1 .

a list of manually constructed hypernym patterns (e.g.


Using our proposed method, this relation is filtered out because those untrusted sources are discouraged by the trustiness feature, and also because there are contrastive evidence 8 saying that 'flying fox' is NOT a 'fox'.

the average trustiness score of LSP method of the sources for the invalid relation (i.e.


Thus, the trustiness feature helps to boost the evidence score for this relation over the threshold value.

the average trustiness score of LSP method (i.e.


Our motivation is to apply a rough but robust reordering to make the source and target sentences have more similar word orders, where fast align can show its power.

seg rev first segments a source-target sentence pair into a sequence of minimal monotone chunk pairs 6 based on the automatically generated word alignment.


Boosted by early successes in language modelling for speech recognition (Schwenk, 2007;Le et al., 2011), NNs have since been successufully applied to many other tasks (Socher et al., 2013;Huang et al., 2012;Yang et al., 2013).

these techniques have been applied to Statistical Machine Translation (SMT), first to estimate continuous-space translation models (CTMs) Le et al., 2012;Devlin et al., 2014), and more recently to implement end-to-end translation systems (Cho et al., 2014;Sutskever et al., 2014).


For each MT system from the selected top 5 system A-E, we paraphrase its translations using the paraphrasing model and lattice decoding separately, aiming to compare the performances of the two models on each MT system.


4 The correlation is computed using the annotations for which scores are available in the corpus (prs).

the corpus acts as a scoring function for the available reference translation pairs, which gives a similarity score between a reference and a translation.


In this paper, we develop a supervised learning technique that improves noisy phrase translation scores obtained by phrase table triangulation.

we extract word translation distributions from small amounts of source-target bilingual data (a dictionary or a parallel corpus) with which we learn to assign better scores to translation candidates obtained by triangulation.


In this paper, we develop a supervised learning algorithm that corrects triangulated word translation probabilities by relying on word translation distributions w sup derived from the limited sourcetarget data.

we represent source and target words using word embeddings (Mikolov et al., 2013) and learn a transformation between the two embedding spaces in order to approximate w sup , thus down-weighting incorrect translation candidates proposed by triangulation ( §3).


In order to generalize, we abstract from words to vector representations of words.

we constrain q to the following parameterization: Here, the vectors v s and v t represent monolingual features and the vector f st represents bilingual features.


This work focuses on the task of finding latent vector representations of the words in a corpus.

we address the issue of what to do when there are multiple languages in the corpus.


The L max parameter controls the maximum latency of the system.

the maximum number of tokens the system is permitted to fall behind the current position.


This lack of diversity is due to the constraint that competing string-to-tree rules must have the same aligned non-terminal symbols, which is taken into account when collecting negative samples.

the ambiguity between translation rules in a stringto-tree system is heavily restricted by the target side syntax.


Thus, the linguistic signals of user traits which are conveyed in the original language may not be preserved over translation.

the attributes on which we wish to rely for modelling may be lost.


Europarl-v7 has two peculiarities: firstly, the corpora are aligned at sentence-level; secondly each pair of languages contains English as one of its members: for instance, there is no French/Italian pair.


Given the potential ethical and security risks associated with deceitful interactions, it is important to build computational tools able not only to detect deceivers but also to provide insights into the nature of deceptive behaviors.

information related to the demographics of the deceivers could be potentially useful, as recent studies have shown that online users lie frequently about their appearance, gender, age or even education level.


DOL enables function overloading (again borrowing ideas from programming languages).

one function name can have multiple core-argument specifications.


For both baselines, experiments are conducted using 5-fold cross-validation with the dev set always included in the training data.

we always use the dev set and 4/5 of the test set as training data for each fold.


Evaluation metrics: Evaluation is performed in the setting that a system can choose NOT to answer all problems in the test set.

one has the flexibility of generating answers only when she knows how to solve it or she is confident about her answer.


In this paper, we represent a new recursive neural network architecture that fits squarely in this tradition, but aims to solve a number of difficulties that have arisen in existing work.

the model we propose addresses three issues: 1. how to make the composition functions adaptive, in the sense that they operate adequately for the many different types of combinations (e.g., adjective-noun combinations are of a very different type than VP-PP combinations); 2. how to deal with different branching factors of nodes in the relevant syntactic trees (i.e., we want to avoid having to binarize syntactic trees, 1 but also do not want ternary productions to be completely independent from binary productions); 3. how to deal with uncertainty about the correct parse inside the neural architecture (i.e., we don't want to work with just the best or k-best parses for a sentence according to an external model, but receive an entire distribution over possible parsers).


}, and let S ⊆ N 2 \{(0, 0)} be a set defining the valid match-up operations between x characters and y characters.

when (s, t) ∈ S, then this means we allow matches of subsequences of x of length s and subsequences of y of length t. 1 It is convenient to define a monotone many-tomany alignment of x and y as a 2×k (for k ≥ 1 arbitrary) nonnegative integer matrix A x,y ∈ N 2×k satisfying A x,y 1 k = x y , i.e., the two rows of A x,y sum up to the lengths of the respective strings, 2 and where each column of A x,y lies in S. For any such alignment, we let (x 1 , .


The graph, however, clarifies that Log-chunk-mconv achieves high accuracy with fewer training data converted from logs.

the method Log-chunk-mconv is good at distilling the informative parts and filtering out the noisy parts.


This paper described the design of a publicly usable IM which collects natural annotations for use as training data for another system.

we (1) described how to construct an IM server that suggests OOV word candidates, (2) designed a publicly usable IM that collects logs of user behavior, and (3) proposed a method for using this data to improve word segmenters.


Then, we train two phrase-based translation models (Chinese-English and English-Chinese) on the data obtained from the converted NE dictionary.

we apply two standard log-linear phrase-based SMT models.


In addition to the model parameters, hyperparameters are re-sampled after each training iteration following the discount and strength hyperparameter resampling in a hierarchical Pitman-Yor process (Teh, 2006).

we resample ⟨d p , θ p ⟩, the pair of discount and strength parameters for phrases from a distribution: where [ ] denotes a generalized Pochhammer symbol, and c ⟨s,t⟩ the number of customers of phrase pair ⟨s, t⟩.


On the other hand, our back-off model could directly express phrase pairs of multiple granularities.

a complex noun may be composed of several Hiero rules in the previous model, but it can be directly expressed by a single phrase pair in our model.


In order to model target-side global dependencies, we propose a novel graph-based collective lexical selection framework for SMT.

• First, we propose a translation graph to model not only local associations between sourceside content words and their target translations but also global relatedness among target-side items.


In our algorithm, we assign each node an evidence score in the translation graph, which indicates either the importance of a source word (for a source word node) or the confidence of a target translation being a correct translation (for a target word node).

we perform collective inference on the translation graph as follows: • First, we set initial evidence scores for nodes in the translation graph.


We also use the following two notations: • The evidence vector V: an n-dimensional vector where the ith component V i is the evidence score contained in this node (if node i corresponds to a source word), or the evidence score from the related translations (if node i corresponds to a target translation).

we use V (0) to denote the initial Algorithm 1 Collective Inference in Translation Graph.


In this paper we introduce the task of detecting content-heavy sentences in cross-lingual context.

we develop methods to identify sentences in Chinese for which English speakers would prefer translations consisting of more than one sentence.


[A] Telomeres are structures at the ends of chromosomes, which gradually reduce in length with the aging of the cells and their loss of the ability to divide.

longer telomeres indicate the slower aging of the cells.


These are frequently used in Chinese and would require a relative clause or an additional sentence in English.

we record the maximum number of dependents for the nouns in the sentence.


Recent researches reveal that the parameter tuning algorithms tailored for specific decoding algorithms can be beneficial in general structured prediction problems (Huang et al., 2012), and in machine translation (Yu et al., 2013;Zhao et al., 2014;Liu and Huang, 2014).

liu and Huang (2014) show that by requiring the conventional parameter tuning algorithms to consider the final decoding results (full translations) as well as the intermediate decoding states (partial translations) at the same time, the inexact decoding can be significantly improved so that correct intermediate partial translations are more likely to survive the beam.


A HIERO decoder translates a source sentence by using a synchronous grammar (Figure 1  where the left-hand-side is a non-terminal symbol, and the right-hand-side is a pair of source and target strings, each of which consists a sequence of lexical terminals and non-terminals.

the same subscript on both sides denotes a one-toone correspondence between their non-terminals.


We use word alignment to determine the span references of different spans.

we first add the tuning set into training data, and then run GIZA++ to get the word alignment.


In the same manner, we also assume the volumes that have the same number of chapters are the same.

their volume size should be as similar as possible Input: • N "labeled" tokens in the Bible domain: word w (i) ∈ V, corresponding context C(w (i) ) ⊂ V and (projected) tag set P (i) ⊂ T for i = 1 .


Our combined parser gives accuracies competitive to state-of-the-art deterministic parsers in the literature.

the method of Chen and Manning (2014) is the same as our NN baseline.


In this parsing model, labeled features are restricted to a single edge.

then the dynamic-programming derivation for each incomplete span becomes where l(s, t) = argmax l∈L S sib (s, t, l).


In online unsupervised DA, domain adaptation is performed incrementally as data comes in.

we adopt a form of representation learning.


We next try to directly learn trees containing the error suffixes (except for deleted tokens).

we use the original Foreebank trees containing the error suffixes for training and evaluate against Foreebank trees containing the error suffixes.


The automatically-parsed data is used to acquire statistics about lexical interactions, which are too sparse to estimate well from any realistically-sized Treebank.

we attempt to infer a function assoc(head, modif er) measuring the "goodness" of head-modifier relations ("how good is an arc in which black is a modifier of jump").


We depart from their work by extending the scoring to include a wider lexical context.

given the sentence fragment in Figure 1, we score the (incorrect) dependency arc (black, jump) based on the triplets (the black fox, will jump over).


However, we would like to go a step further and take into account also the context in which these words occur.

our complete model attempts to estimate the association between word trigrams centered around the head and the modifier words.


We do this by experimenting with novel features, additional transition systems and by testing on a wider array of languages.

we introduce set-valued features to encode the predicted morphological properties and part-ofspeech confusion sets of the words being parsed.


We define the feature group morph as the matrix X morph such that, for where N f is the number of morphological features active on the token indexed by f .

we embed a bag of features into a shared embedding space by averaging the individual feature embeddings.


Inspired by the work of Bohnet and Nivre (2012), we embed the set of top tags according to a first-stage tagger.

we define the feature group ktags as the matrix X ktags such that, for where P(POS = v | f ) is the marginal probability that the token indexed by f has the tag indexed by v, according to the first-stage tagger.


We first rewrite the context window c as: where we replace the average of the word embeddings with a weighted sum of the individual word embeddings within the context.

each word is w i at relative position i is attributed an attention level representing how much the attention model believes this it is important to look at in order to predict the center word.


(2015) in their recent 'bake-off' of leading dependency parsing models.

we use the OntoNotes corpus converted into dependencies using the ClearNLP 3.1 converter, with the train / dev / test split of the CoNLL 2012 shared task.


Different from mapping representations from the source side to the target side by learning a linear matrix on word alignments (Mikolov et al., 2013b), we project source multiword phrase representations onto the target semantic space in a nonlinear manner as we believe that nonlinear relations between languages are more reasonable.

we use a neural network to achieve this goal.


We incorporate learned semantic representations of nonterminals into hierarchical rules.

ordinary hierarchical rules take the following form: where a/b, c/d are strings of terminals on the source and target side, s and t are placeholders denoting the nonterminal X on the source or target side, X s and X t are aligned to each other.


Given a phrase vector representation p and nonterminal X semantic vector v x , Cosine Similarity (CS) is computed as: We set α for the Cosine Similarity between the glue rule and its corresponding phrase as follows: As for Euclidean Distance (ED), it is computed according to the following formula: and similarly we set β for glue rules: We incorporate the proposed model as a new feature into the hierarchical phrase-based translation system.

two features are added into the baseline system: 1.


While these models differ in how the context vector c t is derived, they share the same subsequent steps.

given the target hidden state h t and the source-side context vector c t , we employ a simple concatenation layer to combine the information from both vectors to produce an attentional hidden state as follows: The attentional vectorh t is then fed through the softmax layer to produce the predictive distribution formulated as: We now detail how each model type computes the source-side context vector c t .


As a result of sigmoid, To favor alignment points near p t , we place a Gaussian distribution centered around p t .

our alignment weights are now defined as: We use the same align function as in Eq.


ment) depends on the meanings of its constituents.

the approach models document representation in two steps.


One could also use tree-based composition method such as Recursive Neural Tensor Network (Socher et al., 2013b) or Tree-Structured LSTM (Tai et al., 2015;Zhu et al., 2015) as alternatives.

we try CNN with multiple convolutional filters of different widths (Tang et al., 2015) to produce sentence representation.


The approach is analogous to the recently emerged LSTM (Graves et al., 2013;Zaremba and Sutskever, 2014;Xu et al., 2015) and gated neural network (Cho et al., 2014;Chung et al., 2015).

the transition function of the gated RNN used in this work is calculated as follows.


The composed document representations can be naturally regarded as features of documents for sentiment classification without feature engineering.

we first add a linear layer to transform document vector to real-valued vector whose length is class number C. Afterwards, we add a sof tmax layer to convert real values to conditional probabilities, which is calculated as follows.


We conduct experiments on large-scale datasets consisting of document reviews.

we use one movie review dataset from IMDB (Diao et al., 2014) and three restaurant review datasets from Yelp Dataset Challenge in 2013, 2014 and 2015.


Therefore, we take Conv-GRNN as an example and compare different neural models for document composition in this part.

after obtaining sentence vectors with convolutional neural network as described in Section 2.1, we carry out experiments in following settings.


CRF Baseline: We use a linear-chain CRF (Lafferty et al., 2001) of order 2 as our baseline, which is the state-of-the-art model for opinion target extraction (Pontiki et al., 2014).

the CRF generates (binary) feature functions of order 1 and 2; see (Cuong et al., 2014) for higher order CRFs.


Linguistic Features in RNNs: In addition to the neural features, we also explore the contribution of simple linguistic features in our RNN models using the architecture described in Section 3.6.

we encode four POS tag classes (noun, adjective, verb, adverb) and BIO-tagged chunk information (NP, VP, PP, ADJP, ADVP) as binary features.


Alternatively, this paper proposes an effective, yet simple way to enrich SCFG in hierarchical phrase-based SMT for better semantic parsing.

since the translation rules play a critical role in SMT, we explore to improve translation rule quality and increase its coverage in three ways.


state :: that :::::: border , state@1 ::::::::: l.. one@1 ::::::: p.. 1@1 state@1 n..@1 s..@1 t..@s Examples of glue rules in enriched SCFG.

we omit \Fm and /An if m = 0 and n = 0 respectively.


Evaluation We use the standard evaluation criteria for evaluation by executing both the predicted MRL and the gold standard against the database and obtaining their respective answer.

we convert a translation from MRL into MRL (if exists).


8 Effect of Enriched SCFG From Table 2, we observe that enriched SCFG systems outperform non-enriched SCFG systems over all alignment settings, indicating the effect of enriching nonterminals.

for tgt2src alignment, it obtains improvements of 3.5% in accuracy and 2.7% in F1.


We have also tested our approach on the same dataset with other three languages.

while we are not aware of public resources to looking for semantically close words in German, Greek and Thai, we translate unknown words into null for the three languages.


competitive performance when all the extensions (described in Section 3) are used.

it significantly outperforms the semantic parser with standard SMT components (Andreas et al., 2013).


We then use a deterministic rule to map bridged relations in the intermediate representation to the correct completed relations in the final representation.

we map bridged to Equals if the two children concepts are of type number, and to IsA if the concepts are of type entity (e.g.


This task requires to choose the correct linguistic representation of a sentence given a visual context depicted in a video.

provided with a sentence, n candidate interpretations of that sentence and a video that depicts the content of the sentence, one needs to choose the interpretation that corresponds to the content of the video.


In earlier work, this was not required because the sentences tested in that work were designed to distinguish objects based on constraints rather than identity.

there might have been two different people but they were distinguished in the sentence by their actions or appearance.


We present a novel framework for studying ambiguous utterances expressed in a visual context.

we formulate a new task for resolving structural ambiguities using visual signal.


To construct a feature vector for a source-target pair (s j , t j ), SFE takes the subgraphs G s j and G t j and merges them on the intermediate nodes i.

if an intermediate node i is present in both G s j and G t j , SFE takes the path types π corresponding to i and combines them (reversing the path type coming from the target node t j ).


Relying on the notation introduced earlier, these features are generated by intersecting the subgraphs G s and G t on the intermediate nodes.

when the subgraphs share an intermediate node, we combine the path types found from the source and target to that node.


To get these comparison features, we instead intersect the subgraphs on the path types, and combine the intermediate nodes when there are common path types.

if we see a common path type, such as -GENDER-, we will construct a feature representing a comparison between the intermediate node for the source and the target.


We propose that text collections and associated metadata can be analyzed to reveal optimizing behavior by authors.

we consider the ACL Anthology Network Corpus (Radev et al., 2013), along with author and citation metadata.


Firstly, while they can be pretrained with large amounts of data to learn semantic and syntactic similarities between words, each vector is independent.

even though models based on word lookup tables are often observed to learn that cats, kings and queens exist in roughly the same linear correspondences to each other as cat, king and queen do, the model does not represent the fact that adding an s at the end of the word is evidence for this transformation.


Following Collobert and Weston (2008), we convert the unsupervised learning problem to a supervised one by corrupting training sentences.

for each sentence s we create two sets of negative examples.


Topics are concepts or themes which summarize the content of a group of posts.

a topic is a set of words which frequently co-occur in posts which are similar in content and other conversation regularities.


We show that our model performs well and sidesteps a number of limitations of prior topic segmentation approaches.

: • Our models perform joint topic segmentation and topic labeling while most existing models identify unlabeled segments.


While this CFG defines the structure of conversation trees, by itself this grammar is insufficient for our task.

it contains a single nonterminal of each type (S, X, T , C) and so does not distinguish between topics.


All STRUCTURE trees contain thread structure but no topic labels.

they have coarse non-terminals (X, T and S) only.


For imbalance in the number of topics and documents, C-LDA mimics document over-sampling in the Gibbs sampler using a different unit-value in the word count table for each collection.

a unit η c is chosen for each collection such that the average equivalent number of assigned words per-topic This process both increases the topic quality (in terms of collection balance) in the resulting heldout perplexity of the model.


Tensor-based feature mapping Since each ngram in the sequence is now expanded into a high-dimensional tensor using tensor products, the set of filters are analogously maintained as highorder tensors.

our filters are linear mappings over the higher dimensional interaction terms rather than the original word coordinates.


To this end, we assume a low-rank factorization of the tensor T, represented in the Kruskal form.

t is decomposed into a sum of h rank-1 tensors to see this, consider word vectors with a "bias" term xi = [xi; 1].


Second, we stack multiple tensorbased feature mapping layers.

the input sequence x is first processed into a feature sequence and passed through the non-linear transformation to obtain z (1) .


Word vectors The word vectors are pre-trained on much larger unannotated corpora to achieve better generalization given limited amount of training data .

for the English sentiment classification task, we use the publicly available 300-dimensional GloVe word vectors trained on the Common Crawl with 840B tokens (Pennington et al., 2014).


Our full model obtains the highest accuracy on both the development and test sets.

it achieves 51.2% and 88.6% test accuracies on fine-grained and binary tasks respectively 5 .


To see how our model captures the sentiment at different local context, we apply the learned softmax activation to the extracted features at each position without taking the average.

for each index i, we obtain the local sentiment p = softmax W z (1)  .


We use two types of building blocks, block A and block B , as shown in Figure 3.

we define block A as an instance of block A has three convolution layers, one corresponding to each of the three pooling functions; all have the same window size ws a .


1 We use blocks of type A for all holistic convolution layers.

we define block B as block B contains two groups of convolution layers of width ws b , one with max-pooling and one with min-pooling.


Each group B ( * ) contains a convolution layer with Dim per-dimension convolution filters.

we use blocks of type B for convolution layers that operate on individual dimensions of word vectors.


For a group A (ws a , pooling a , sent) with a convolution layer with numFilter A filters, we define the output oG A as a vector of length numFilter A where entry j is where filters are indexed as F j .

the output of group A ( * ) is a numFilter A -length vector containing the output of applying the pooling function on each filter's vector of filter match outputs.


We can analyze these models in the binary calibration framework ( §2-3) by looking at marginal distribution of binary-valued outcomes of parts of the predicted structures.

we examine calibration of predicted probabilities of individual tokens' tags ( §4.2.2), and of pairs of consecutive tags ( §4.2.3).


Similarly, all the above concepts have their pointwise counterparts, obtained by dropping the expectation operator.

the pointwise interaction information is defined as PInt(x 1 , x 2 , y) = PMI(y; x 1 , x 2 ) − PMI(y; x 1 ) − PMI(y; x 2 ) = log P (x 1 )P (x 2 )P (y)P (x 1 ,x 2 ,y) P (x 1 ,x 2 )P (x 1 ,y)P (x 2 ,y) .


In this paper, we propose a novel BO-CPD model that improves the detection of change points in continuous signals by incorporating the rich external information implicitly written in texts on top of the long-term change analysis of the GP.

our model finds causes of signal changes in news articles which are influential sources of markets of interests.


Traditional approaches hardly concern the existence of heterogeneous resource and are difficult to relate different annotation schemas, but in the framework of our model, heterogeneous data can be introduced in a relatively convenient way.

we learn a bidirectional LSTM RN-N model based on heterogeneous data, then with the fine-tuned word embeddings we initialize the model on our experimental dataset.


As a freely downloadable resource, the corpus is annotated on top of PropBank, which uses the WSJ section of the Penn Tree-Bank.

negation focus annotation on this corpus is restricted to verbal negations (Blanco and Moldovan, 2011).


We also introduce simple but highly effective models for both tasks that outperform strong baselines.

our factuality regression model uses a learning objective that combines the advantages of LASSO and support vector regression, enabling it to effectively consider sparse lexical cues.


7 To better understand the difference be-tween the two settings, we analyze the ranks individually, rather than reporting aggregated metrics (Mean and Hits@10).

we distribute the 10,000 instances into different bins according to the ranks given by one setting (e.g.


When learning concept A, if one needs to refer to concept B for a lot of A's related concepts but not vice versa, B would more likely be a prerequisite of A than A of B.

we model a concept in a vector space using its related concepts and measure the prerequisite relation between two concepts by computing how differently the two's related concepts refer to each other, or reference distance (RefD).


As a widely used open-access encyclopedia, Wikipedia provides relatively up-to-date and high quality knowledge and has been successfully utilized as explicit concepts (Gabrilovich and Markovitch, 2007).

moreover, the rich hyperlinks created by Wiki editors provide a natural way to calculate the reference indicator function r. the concept space C consists of all Wikipedia articles.


We further explore the performance of RefD through a case study for the concept "deep learning" (denoted as c ).

for any concept c linked from c we calculate RefD(c , c).


(2014) investigated the normalisation of medical terms in Twitter messages.

they proposed to use the Lucene retrieval engine 3 to retrieve medical concepts that could be potentially mapped to a given Twitter phrase, when mapping between Twitter phrases and medical concepts.


(2003) for the medical text normalisation task.

we use the phrase-based MT technique to translate phrases from Twitter language to formal medical language, before mapping the translated phrases to medical concepts based on the ranked similarity of their word vector representations.


Hence, we evaluate our approach using Mean Reciprocal Rank (MRR) measure (Craswell, 2009), which is an information retrieval measure based on the user model where the user wants to see only one relevant concept.

mRR is based on the the reciprocal of the rank at which the first relevant concept is viewed in the ranking (e.g.


We use three different techniques, including onehot, CBOW and GloVe, to create word vector representations used in our approach (see Section 3).

the vocabulary for creating the one-hot representation includes all terms in the Twitter phrases and the descriptions of the target SNOMED-CT concepts.


(2003), as implemented in the Moses toolkit (Koehn et al., 2007) 9 with default settings, to learn to translate from the Twitter language to the medical language.

when training the translator, we show the learner pairs of the Twitter phrases and descriptions of the corresponding SNOMED-CT concepts.


Our results suggest that simply increasing the dimensionality of baseline skip-gram embeddings is sometimes sufficient to achieve the same performance wins that come from using multi-sense embeddings.

the most straightforward way to yield better performance on these tasks is just to increase embedding dimensionality.


our conclusions are based on the pipelined system that we introduce, and other multi-sense embedding systems (e.g., a more advanced sense learning model or a better sense label model or a completely different pipeline system) may find stronger effects of multi-sense models.


Kiela and Clark (2013) present a model of detecting non-compositionality based on the hypothesis that the average distance between a phrase vector and its substituted phrase vectors is related to its compositionality.

compositional phrases are less similar to their neighbors in semantic space.


Then this same error for this same task (norm 2 between predicted and unsupervised compound embeddings) is used to measure noncompositionality.

we learn a composition function (with several models) and identify non-compositional expressions as those for which the error of this composition function is high.


Then we sort the compounds by their error and set the λ ij of the biggest B elements to 0, and the rest to 1.

we assume the compounds with big error are presumably non-compositional according to what we know until that iteration.


In this paper, we present a novel approach which can solve a general class of arithmetic problems without predefined equation templates.

it can handle multiple step arithmetic problems as shown in Example 1.


Learning to generate the simpler decomposed expressions allows us to support generalization across problems types.

our system could solve Example 1 even though it has never seen a problem that requires both addition and multiplication operations.


Our constrained inference module falls under the general framework of Constrained Conditional Models (CCM) (Chang et al., 2012).

we use the L + I scheme of CCMs, which predicts structured output by independently learning several simple components, combining them at inference time.


Question Features: Finally, we add a few features based on the question asked.

for arithmetic problems where only one operation is needed, the question contains signals for the required operation.


In particular, for arithmetic problems where only one operation is needed, the question contains signals for the required operation.

we add indicator features based on whether the question mentions comparison-related tokens (e.g., "more", "less" or "than"), or whether the question asks for a rate (indicated by tokens such as "each" or "one").


First, for each problem, we replace the numbers and nouns with the part of speech tags, and then we cluster the problems based on unigrams and bigrams from this modified problem text.

we cluster problems together whose unigram-bigram similarity is over 90%.


This theory underlies our algorithmic solution -we develop classifiers and a constrained inference approach that exploits redundancy in the information, and show that this yields strong performance on several benchmark collections.

our approach achieves state of the art performance on two publicly available arithmetic problem datasets and can support natural generalizations.


In particular, our approach achieves state of the art performance on two publicly available arithmetic problem datasets and can support natural generalizations.

our approach performs competitively on multistep problems, even when it has never observed the particular problem type before.


Although we develop and use the notion of expression trees in the context of numerical expressions, the concept is more general.

if we allow leaves of expression trees to represent variables, we can express algebraic expressions and equations in this framework.


The matrix is populated such that in each row the values of each column group sum up to unity, i.e., we normalize values within column groups.

we set to 1 the values of the variable of the relation and the tuple of the corresponding fact.


CORE employs a matrix factorization model based on factorization machines and the open-world assumption to capture latent semantic information about the individual variables.

we associate with each variable v ∈ V a bias term b v ∈ R and a latent feature vector f v ∈ R d , where the dimensionality d of the latent feature space is a hyperparameter of our model.


As indicated above, our model overcomes the key problem that there is no observation, and thus no context, for r(t) by context aggregation and sharing.

we create an test pointx for tuple r(t) in a way similar to creating data points, i.e., we set the relation, tuple, and entity variables accordingly and add the aggregated, normalized context of t. Once test pointx has been created, we can predict its score s(x) using Eq.


In essence, the BPR criterion aims to maximize the average "difference" ln σ(δ(x, x − )) between the score of fact x and each of its negative samples x − , averaged over all facts.

we aim to score x higher than each x − .


This allows us to avoid constructing the sets X − x , which are often infeasibly large, and worked well in our experiments.

in each stochastic gradient step, we randomly sample a training point x ∈ X, and subsequently randomly sample a negative sample x − ∈ X − x .


Both children and adults make various inferences about what a fep is depending on the levels of the taxonomy from which the training instances are drawn.

x&T use a taxonomy with animals, vehicles, and vegetables, from which instances are drawn to produce the training conditions in Fig.


Smoothing entails that features previously unseen with a word (all f i such that assoc t (f i , w j ) = 0) have a small but non-zero probability.


This is especially an issue for features from a category hierarchy, where features of a subordinate category should not compete with features of the parent.

while a higher probability of DALMATIAN features (e.g., black spotted coat) may lessen the likelihood of BULLDOG features (e.g., wrinkles), it should not decrease the probability of DOG features (e.g., having 4 legs).


5 In contrast, we assume that adults, through word learning experience, have accumulated biases that reflect observed differences in feature groups.

more specifically, we assume that the probability of observing a new feature for a group G depends on the degree of specificity of that group: over time, it is less likely to observe a completely new kind of animal, e.g., than a new breed of dog.


Based on this observation, we hypothesize that people generalize less to a basic-level category when their mental representations for that category's instances have more distinguishing features.

we assume that people differentiate the given instances of the category "containers" more than those for "clothing" and "seats".


However, in a 2 s , w r became erroneously aligned, because the alignment distribution 3 of w r was only learned from a 1 s , thus consisted of non-zero values only for generating the target-side words in s. Therefore, the alignment probabilities from the rare word w r to the unaligned words in s were extraordinarily high, since almost all of the probability mass was distributed among them.

the story behind these garbage collector effects is that erroneous alignments are able to provide support for themselves; the probability distribution learned only from s is re-applied to s. In this way, these "garbage collector effects" are a form of over-fitting.


This leads to rare words being passively aligned.

the leave-one-out method would align rare words unless the confidence is high.


We find that our approach systematically outperforms GIZA++ and BERKELEY on all NIST datasets.

generalizing the agreement to model the discrepancy between word alignment and phrase segmentation is consistently beneficial for improving translation quality, suggesting that it is important to introduce structural constraints into word alignment to increase the correlation between alignment and translation.


As a result, we extract language model features by using word-based language model learned from a large quantity of standard texts.

1-gram, 2-gram, 3-gram features are extracted.


The effect of language model From Table 4, we observe that the performances increase when using language model features.

the  Furthermore, we also find that the language model features are helpful to identifying the formal words, as shown in Table 5.


The performance of our model is the best among the three systems.

the precision in our SNT model improves upon the baselines significantly.


This tensor scoring method avoids the parameter explosion and overfitting problem by assuming a low-rank factorization of the parameters A.

a is decomposed into the sum of r rank-1 components: where r is the rank of the tensor, U, V ∈ R r×n and W ∈ R r×l are parameter matrices.


Table 2 summarizes the notations of the feature vectors and the corresponding parameters.

for each arc h → m with label l, we first compute the intermediate feature embedding e 1 that captures the interaction between the head φ h , the modifier φ m and the arc direction and length φ d , by an element-wise product.


This decomposition provides another view of our tensor model.

our hierarchical tensor is algebraically equivalent to the sum of three multiway tensors, where H c , M c and L are shared.


We compute the final score as the interaction between the delexicalized feature embedding in Equation 5 and the lexical components.

: where e 5 is the embedding that represents the delexicalized transfer results.


We use the CCA method (Faruqui and Dyer, 2014) to generate multilingual word embeddings.

we project word vectors in each non-English language to the English embedding space.


Then we integrate them to automatically learn the compositions of the dense features for transition-based dependency parsing.

tree-GRNN models the feature combinations for the trees in stack, which already have partial dependency structures.


In neural network architecture, the words, POS tags and arc labels are mapped into distributed vectors (embeddings).

given the word embedding matrix E w ∈ R de×nw , each word w i is mapped into its corresponding column e w w i ∈ R de of E w according to its index in the dictionary, where d e is the dimensionality of embeddings and n w is the dictionary size.


In the simplest case, children nodes are combined into their parent node using a weight matrix W which is shared across the whole network, followed by a non-linear function g(•).

given the left child node vector h L ∈ R d and right child node vector h R ∈ R d , their parent node vector h P ∈ R d will be formalized as: where W ∈ R d×2d and g is a non-linear function as mentioned above.


where  where word embedding e w p.w ∈ R de , pos embedding e t p.t ∈ R de and label embedding e l p.l ∈ R de are extracted from embedding matrices E w , E t and E l according to the indices of the corresponding word p.w, pos p.t and label p.l respectively.

in the case of unlabeled attachment parsing, we ignore the last term e l p.l in Eq.


For parameter initialization, we use random initialization within (-0.01, 0.01) for all parameters except the word embedding matrix E w .

we adopt pre-trained English word embeddings from (Collobert et al., 2011).


The experiment results show the effectiveness of our proposed two heterogeneous gated recursive neural networks.

plain parser is as same as parser of Chen and Manning (2014).


However, previous works are focused on maximizing performance in the same domain as that of the training data.

it is uncertain that these technologies robustly support out-of domain tasks.


Using downstream information : Hypothesis selection for the input to the SMT system should be done jointly by the ASR and the SMT systems.

there may exist hypotheses that a trained SMT system may find easier to translate and produce better translations for than the ones that are deemed best based on the ASR acoustic and language model scores.


We start with the previously discussed idea of a phrase lattice, using weights from the ASR system only.

now, we use the weighted phrase acceptorW M T to bring in the SMT features 4 .


We study the effectiveness of word embeddings to overcome this disadvantage of ROUGE.

instead of measuring lexical overlaps, word embeddings are used to compute the semantic similarity of the words used in summaries instead.


Experimenting on the TAC AESOP dataset, we show that this proposal exhibits very good correlations with human assessments, measured with the Spearman and Kendall rank coefficients.

rOUGE-WE-1 outperforms leading state-of-theart systems consistently.


They are only dependent on the time window using an exponential decay function, µ m = exp(−0.5 × m) where m stands for the mth epoch counting backwards in the past M epochs.

more recent documents would have a relatively stronger influence on the model parameters in the current epoch compared to earlier documents.


Dynamic LDA (DLDA) 2 : this is the dynamic LDA (Blei and Lafferty, 2006) where the topic-word distributions are linked across epochs based on the Markovian assumption.

the topic-word distribution at the current epoch is only influenced by the topicword distribution in the previous epoch.


To get the probability of some w given an unordered subtree u, we want to calculate the probability of w given that G has generated the particular multiset W of dependents corresponding to u.

to do this, we calculate: where and W is the set of all possible configurations (w l , w r ) compatible with multiset W. W is the set of pairs of permutations of multisets W l and W r for all possible partitions of W into W l and W r .


Several studies (Lerman and McDonald, 2009;Haghighi and Vanderwende, 2009) have addressed summarization using KL-divergence, but they calculated KLdivergence based on word distributions in a discrete space.

our study is the first attempt to summarize by asymptotically estimating KL-divergence based on embedding distributions in a continuous space.


While finite-state tree automata correspond to a single concept, and share all the nice properties of string automata (Comon et al., 2007), the situation with tree-to-tree or tree-to-string transducers is more complicated (Maletti, 2010;Graehl et al., 2008): several variants exist, only some of which support the operations that our conceptual model requires (composition with the string transducer σ and intersection with the tree automaton ω).

the "linear non-deleting topdown tree transducers" defined in (Maletti, 2010) 9 have the requisite properties.


We investigate the differences between male and female character depictions in movies, based on patterns of language used.

we use an automatically generated lexicon of linguistic norms characterizing gender ladenness.


We apply Jensen-Shannon divergence and cosine similarity on our proposed venue representations to analyze the venues over time.

we ask the following questions: What are the popular topics in ACL and EMNLP in a particular year?


Unlike QASENT, we did not filter questions with only incorrect answers, as they are still valuable for model training and more importantly, useful for evaluating the task of answer triggering, as described in Section 3.

we find nearly two-thirds of questions contain no correct answers in the candidate sentences.


We employ precision, recall and F 1 scores for answer triggering, at the question level.

we compute these metrics by aggregating all the candidate sentences of a question.


In this work we address the second task, aiming to identify translations each user is more likely to prefer.

1 we consider a setting where at least two MT systems are available, and the goal is to predict which of the translation systems the user would choose, assuming we have no knowledge about her preference between them.


Our method, denoted CTP (Collaborative Translational Preferences), is based on a k-nearest-neighbors approach for user-user CF.

we predict the translational preferences of a user based on those of similar users.


E.g., for en-es, where there are only 57 users in total, reducing k's value from 50 to 25, improves results of CTP from 62.6% to 63.2%, higher than all other methods (whose scores are not affected).

in comparison to AVPF, weighting by the similarity scores was found to be a more significant factor than selecting a small subset of the users.


The work presented in this paper provides additional insight into this question by controlling for these factors in a different way than previous work and by examining multiple communities of interest.

using data from Reddit discussion forums, we look at the role of author reputation as measured in terms of a karma k-index, and control for topic and timing by ranking comments in a constrained window within a discussion.


the languages with the least amount of training data.

for French, the precision of the combination system (0.36) is double that of the 4-gram model (0.18) with only a 34% loss in recall (0.24 to 0.16).


We note that the batch augmentation in case of PRA-SVO and PRA-VS, and embedding computation in case of PRA-VS are all specific to the relations in the evaluation set, and hence can't be ignored as a one-time offline cost.

these costs are likely to increase as more relations (and their instances) are included during training and testing.


In addition, we introduce a similar approach that instead uses the same objective function that was used to learn the original skip-gram embeddings.

we first train a standard skip-gram model, and then learn from the additional contexts in a second training stage as if they form a separate corpus: We call this approach skip-gram retrofitting.


Although this modularity is preferable from development perspectives, the pipelined model limits the interactions.

the first stage alone is unlikely to detect E2 and E4 as events due to the difficulties described above.


Their underlying ideas are similar to ours.

one can train a structured learning model to globally capture the interactions between two relevant tasks via a certain kind of structure, while making predictions specifically for these respective tasks.


We attribute this result to the small size of the development data.

the development data has only 28 event coreferences, which makes it difficult to reveal the effect of beam width.


Our representation encodes syntactic and morphological structure jointly, allowing a single model to learn the translation of both.

we work with a string-to-tree model with GHKM-style rules (Galley et al., 2006), and a relational dependency language model (Sennrich, 2015).


We ensure that each virtual node dominates the head of the full constituent through a mixed binarization.

3 we perform right binarization of the head and all pre-modifiers, then left binarization of all post-modifiers.


Our final encoding scheme, No-Repeats, uses a different vocabulary for the first and second symbols in each rare word.

rare words are represented as "sX tY," where X and Y are integers that may be the same or different.


Therefore, new studies are needed to provide a more complete picture regarding regularization for neural natural language processing.

we focus on the following research questions in this paper.


• Re-embedding words does not improve generalization.

in Experiment II, the ultimate accuracy is improved by 0.44, which is not large.


6 Semi-supervised Learning 6.1 Self-training Given the small amount of gold labeled data from the AOC corpus and large amount of unlabeled data from the social media platform, a natural combination is semi-supervised learning.

by applying the strongly supervised classifier on the unlabeled data, we can obtain "automatically labeled" dialect data that could further improve the classification accuracy.